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How to measure anything finding the value of intangibles in business, second edition

HOW TO
M E A SUR E
A N Y TH I NG
F I N D I N G T H E VA L U E O F
“ I N TA N G I B L E S ” I N B U S I N E S S

2nd
Edition
REVISED,
EXPANDED &
SIMPLIFIED

DOUGLAS W. HUBBARD



How to Measure Anything
Finding the Value of
“Intangibles” in Business

Second Edition


DOUGLAS W. HUBBARD

John Wiley & Sons, Inc.


Copyright

C

2010 by Douglas W. Hubbard. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data:


Hubbard, Douglas W., 1962How to measure anything : finding the value of “intangibles” in business /
Douglas W. Hubbard. – 2nd ed.
p. cm.
Includes index.
ISBN 978-0-470-53939-2 (cloth)
1. Intangible property–Valuation. I. Title.
HF5681.I55H83 2010
657 .7–dc22
2009051051

Printed in the United States of America.
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2 1


I dedicate this book to the people who are my inspirations
for so many things: to my wife, Janet, and to our children,
Evan, Madeleine, and Steven, who show every potential for
being Renaissance people.
I also would like to dedicate this book to the military men
and women of the United States, so many of whom I know
personally. I’ve been out of the Army National Guard for
many years, but I hope my efforts at improving battlefield
logistics for the U.S. Marines by using better measurements
have improved their effectiveness and safety.



Contents

Preface

xi

Acknowledgments

xv

SECTION I

MEASUREMENT: THE SOLUTION EXISTS

1

CHAPTER 1

Intangibles and the Challenge

3

Yes, I Mean Anything
The Proposal

5
6

An Intuitive Measurement Habit: Eratosthenes,
Enrico, and Emily

9

CHAPTER 2

How an Ancient Greek Measured the Size of Earth
Estimating: Be Like Fermi
Experiments: Not Just for Adults
Notes on What to Learn from Eratosthenes,
Enrico, and Emily
CHAPTER 3

The Illusion of Intangibles:
Why Immeasurables Aren’t
The Concept of Measurement
The Object of Measurement
The Methods of Measurement
Economic Objections to Measurement
The Broader Objection to the Usefulness
of “Statistics”

10
11
13
18

21
22
26
28
35
37

v


vi

Contents

Ethical Objections to Measurement
Toward a Universal Approach to Measurement

39
41

SECTION II

BEFORE YOU MEASURE

45

CHAPTER 4

Clarifying the Measurement Problem

47

Getting the Language Right: What “Uncertainty”
and “Risk” Really Mean
Examples of Clarification: Lessons for Business
from, of All Places, Government
CHAPTER 5

CHAPTER 6

CHAPTER 7

49
51

Calibrated Estimates: How Much Do You Know Now?

57

Calibration Exercise
Further Improvements on Calibration
Conceptual Obstacles to Calibration
The Effects of Calibration

59
64
65
71

Measuring Risk through Modeling

79

How Not to Measure Risk
Real Risk Analysis: The Monte Carlo
An Example of the Monte Carlo Method and Risk
Tools and Other Resources for Monte
Carlo Simulations
The Risk Paradox and the Need for Better
Risk Analysis

79
81
82

Measuring the Value of Information

99

The Chance of Being Wrong and the Cost of
Being Wrong: Expected Opportunity Loss
The Value of Information for Ranges
The Imperfect World: The Value of Partial
Uncertainty Reduction
The Epiphany Equation: How the Value of
Information Changes Everything
Summarizing Uncertainty, Risk, and Information
Value: The First Measurements

91
93

100
103
107
110
114


vii

Contents

SECTION III

MEASUREMENT METHODS

117

CHAPTER 8

The Transition: From What to Measure to
How to Measure

119

Tools of Observation: Introduction to the
Instrument of Measurement
Decomposition
Secondary Research: Assuming You Weren’t the
First to Measure It
The Basic Methods of Observation: If One
Doesn’t Work, Try the Next
Measure Just Enough
Consider the Error
Choose and Design the Instrument
CHAPTER 9

CHAPTER 10

Sampling Reality: How Observing Some Things
Tells Us about All Things

120
124
127
128
131
132
136

139

Building an Intuition for Random Sampling: The
Jelly Bean Example
A Little about Little Samples: A Beer Brewer’s
Approach
Statistical Significance: A Matter of Degree
When Outliers Matter Most
The Easiest Sample Statistics Ever
A Biased Sample of Sampling Methods
Measure to the Threshold
Experiment
Seeing Relationships in the Data: An
Introduction to Regression Modeling
One Thing We Haven’t Discussed—and Why

142
145
148
150
153
162
165

Bayes: Adding to What You Know Now

177

Simple Bayesian Statistics
Using Your Natural Bayesian Instinct
Heterogeneous Benchmarking: A “Brand
Damage” Application
Bayesian Inversion for Ranges: An Overview

178
181

141

169
174

187
190


viii

Contents

Bayesian Inversion for Ranges: The Details
The Lessons of Bayes

193
196

SECTION IV

BEYOND THE BASICS

201

CHAPTER 11

Preference and Attitudes: The Softer Side
of Measurement

203

CHAPTER 12

CHAPTER 13

Observing Opinions, Values, and the Pursuit
of Happiness
A Willingness to Pay: Measuring Value
via Trade-offs
Putting It All on the Line: Quantifying
Risk Tolerance
Quantifying Subjective Trade-offs: Dealing with
Multiple Conflicting Preferences
Keeping the Big Picture in Mind: Profit
Maximization versus Purely Subjective Trade-offs

218

The Ultimate Measurement Instrument:
Human Judges

221

203
207
211
214

Homo absurdus: The Weird Reasons behind
Our Decisions
Getting Organized: A Performance
Evaluation Example
Surprisingly Simple Linear Models
How to Standardize Any Evaluation: Rasch Models
Removing Human Inconsistency: The Lens Model
Panacea or Placebo?: Questionable Methods
of Measurement
Comparing the Methods

227
228
230
234

New Measurement Instruments for Management

251

The Twenty-First-Century Tracker: Keeping Tabs
with Technology
Measuring the World: The Internet as an Instrument
Prediction Markets: A Dynamic Aggregation
of Opinions

222

238
246

251
254
257


ix

Contents

CHAPTER 14

APPENDIX

Index

A Universal Measurement Method: Applied
Information Economics

265

Bringing the Pieces Together
Case: The Value of the System that Monitors
Your Drinking Water
Case: Forecasting Fuel for the Marine Corps
Ideas for Getting Started: A Few Final Examples
Summarizing the Philosophy

266

Calibration Tests (and Their Answers)

289

270
275
281
287

299



Preface

A

lot has happened since the first edition of this book was released in
2007. First, my publisher and I found out that a book with the title How
to Measure Anything apparently sparks interest. For three years, the book
has consistently been the single best seller in Amazon’s math for business
category. Interest shows no sign of slowing and, in fact, registrations on the
book’s supplementary Web site (www.howtomeasureanything.com) show
that the interest is growing across many industries and countries. It was
successful enough that I could pitch my second book idea to my editor.
The 2008 financial crisis occurred just as I was finishing my second
book, The Failure of Risk Management: Why It’s Broken and How to Fix It. I
started writing that book because I felt that the topic of risk, which I could
spend only one chapter on in this book, merited much more space. I argued
that a lot of the most popular methods used in risk assessments and risk
management don’t stand up to the bright light of scientific scrutiny. And I
wasn’t just talking about the financial industry. I started writing the book
well before the financial crisis started. I wanted to make it just as relevant
to another Katrina or 9/11 as to a financial crisis.
I’ve also written several more articles, and the combined research from
them, my second book, and comments from readers on the book’s Web
site gave me plenty of new material to add to this second edition. But
the basic message is still the same. I wrote this book to correct a costly
myth that permeates many organizations today: that certain things can’t be
measured. This widely held belief is a significant drain on the economy,
public welfare, the environment, and even national security. “Intangibles”
such as the value of quality, employee morale, or even the economic impact
of cleaner water are frequently part of some critical business or government
policy decision. Often an important decision requires better knowledge of
the alleged intangible, but when an executive believes something to be
immeasurable, attempts to measure it will not even be considered.
As a result, decisions are less informed than they could be. The chance
of error increases. Resources are misallocated, good ideas are rejected, and
bad ideas are accepted. Money is wasted. In some cases life and health are

xi


xii

Preface

put in jeopardy. The belief that some things—even very important things—
might be impossible to measure is sand in the gears of the entire economy.
All important decision makers could benefit from learning that anything they really need to know is measurable. However, in a democracy and a free enterprise economy, voters and consumers count among
these “important decision makers.” Chances are your decisions in some
part of your life or your professional responsibilities would be improved
by better measurement. And it’s virtually certain that your life has already
been affected—negatively—by the lack of measurement in someone else’s
decisions.
I’ve made a career out of measuring the sorts of things many thought
were immeasurable. I first started to notice the need for better measurement in 1988, shortly after I started working for Coopers & Lybrand as a
brand-new MBA in the management consulting practice. I was surprised
at how often clients dismissed a critical quantity—something that would
affect a major new investment or policy decision—as completely beyond
measurement. Statistics and quantitative methods courses were still fresh in
my mind. In some cases, when someone called something “immeasurable,”
I would remember a specific example where it was actually measured. I
began to suspect any claim of immeasurability as possibly premature, and
I would do research to confirm or refute the claim. Time after time, I kept
finding that the allegedly immeasurable thing was already measured by an
academic or perhaps professionals in another industry.
At the same time, I was noticing that books about quantitative methods didn’t focus on making the case that everything is measurable. They
also did not focus on making the material accessible to the people who
really needed it. They start with the assumption that the reader already believes something to be measurable, and it is just a matter of executing the
appropriate algorithm. And these books tended to assume that the reader’s
objective was a level of rigor that would suffice for publication in a scientific
journal—not merely a decrease in uncertainty about some critical decision
with a method a nonstatistician could understand.
In 1995, after years of these observations, I decided that a market existed for better measurements for managers. I pulled together methods from
several fields to create a solution. The wide variety of measurement-related
projects I had since 1995 allowed me to fine-tune this method. Not only was
every alleged immeasurable turning out not to be so, the most intractable
“intangibles” were often being measured by surprisingly simple methods. It
was time to challenge the persistent belief that important quantities were
beyond measurement.
In the course of writing this book, I felt as if I were exposing a big
secret and that once the secret was out, perhaps a lot of things would be
different. I even imagined it would be a small “scientific revolution” of sorts


Preface

xiii

for managers—a distant cousin of the methods of “scientific management”
introduced a century ago by Frederick Taylor. This material should be even
more relevant than Taylor’s methods turned out to be for twenty-first-century
managers. Whereas scientific management originally focused on optimizing
labor processes, we now need to optimize measurements for management
decisions. Formal methods for measuring those things management usually
ignores have barely reached the level of alchemy. We need to move from
alchemy to the equivalent of chemistry and physics.
The publisher and I considered several titles. All the titles considered
started with “How to Measure Anything” but weren’t always followed by
“Finding the Value of Intangibles in Business.” I give a seminar called “How
to Measure Anything, But Only What You Need To.” Since the methods in
this book include computing the economic value of measurement (so that
we know where to spend our measurement efforts), it seemed particularly
appropriate. We also considered “How to Measure Anything: Valuing Intangibles in Business, Government, and Technology” since there are so many
technology and government examples in this book alongside the general
business examples. But the title chosen, How to Measure Anything: Finding
the Value of “Intangibles” in Business, seemed to grab the right audience
and convey the point of the book without necessarily excluding much of
what the book is about.
The book is organized into four sections. The chapters and sections
should be read in order because the first three sections rely on instructions
from the earlier sections. Section One makes the case that everything is
measurable and offers some examples that should inspire readers to attempt measurements even when it seems impossible. It contains the basic
philosophy of the entire book, so, if you don’t read anything else, read this
section. In particular, the specific definition of measurement discussed in
this section is critical to correctly understand the rest of the book.
Section Two begins to get into more specific substance about how to
measure things—specifically uncertainty, risk, and the value of information.
These are not only measurements in their own right but, in the approach
I’m proposing, prerequisites to all measurements. Readers will learn how
to measure their own subjective uncertainty with “calibrated probability
assessments” and how to use that information to compute risk and the
value of additional measurements. It is critical to understand these concepts
before moving on to the next section.
Section Three deals with how to reduce uncertainty by various methods
of observation, including random sampling and controlled experiments. It
provides some shortcuts for quick approximations when possible. It also
discusses methods to improve measurements by treating each observation
as updating and marginally reducing a previous state of uncertainty. It reviews some material that readers may have seen in first-semester statistics


xiv

Preface

courses, but it is written specifically to build on the methods discussed in
Section Two. Some of the more elaborate discussions on regression modeling and controlled experiments could be skimmed over or studied in detail,
depending on the needs of the reader.
Section Four is an eclectic collection of interesting measurement solutions and case examples. It discusses methods for measuring such things as
preferences, values, flexibility, and quality. It covers some new or obscure
measurement instruments, including calibrated human judges or even the
Internet. It summarizes and pulls together the approaches covered in the
rest of the book with detailed discussions of two case studies and other
examples.
In Chapter 1, I suggest a challenge for readers, and I will reinforce that
challenge by mentioning it here. Write down one or more measurement
challenges you have in home life or work, then read this book with the
specific objective of finding a way to measure them. If those measurements
influence a decision of any significance, then the cost of the book and the
time to study it will be paid back manyfold.


Acknowledgments

S

o many contributed to the content of this book through their suggestions,
reviews, and as sources of information about interesting measurement
solutions. In no particular order, I would like to thank these people:

Freeman Dyson
Peter Tippett
Barry Nussbaum
Skip Bailey
James Randi
Chuck McKay
Ray Gilbert
Henry Schaffer
Leo Champion
Tom Bakewell
Bill Beaver
Julianna Hale
James Hammitt
Rob Donat
Michael Brown
Sebastian Gheorghiu
Jim Flyzik

Pat Plunkett
Art Koines
Terry Kunneman
Luis Torres
Mark Day
Ray Epich
Dominic Schilt
Jeff Bryan
Peter Schay
Betty Koleson
Arkalgud Ramaprasad
Harry Epstein
Rick Melberth
Sam Savage
Gunther Eyesenbach
Johan Braet
Jack Stenner

Robyn Dawes
Jay Edward Russo
Reed Augliere
Linda Rosa
Mike McShea
Robin Hansen
Mary Lunz
Andrew Oswald
George Eberstadt
Grether
David Todd Wilson
Emile Servan-Schreiber
Bruce Law
Bob Clemen
Michael Hodgson
Moshe Kravitz
Michael Gordon-Smith

Special thanks to Dominic Schilt at Riverpoint Group LLC, who saw the
opportunities with this approach back in 1995 and has given so much support since then. And thanks to all of my blog readers who have contributed
ideas for this second edition.

xv



SECTION

Measurement: The
Solution Exists

I



CHAPTER

1

Intangibles and the Challenge
When you can measure what you are speaking about, and express it in
numbers, you know something about it; but when you cannot express
it in numbers, your knowledge is of a meager and unsatisfactory kind;
it may be the beginning of knowledge, but you have scarcely in your
thoughts advanced to the state of science.
—Lord Kelvin, British physicist and member of
the House of Lords, 1824–1907

A

nything can be measured. If a thing can be observed in any way at all,
it lends itself to some type of measurement method. No matter how
“fuzzy” the measurement is, it’s still a measurement if it tells you more
than you knew before. And those very things most likely to be seen as
immeasurable are, virtually always, solved by relatively simple measurement
methods.
As the title of this book indicates, we will discuss how to find the
value of those things often called “intangibles” in business. There are two
common understandings of the word “intangible.” It is routinely applied to
things that are literally not tangible (i.e., not touchable, solid objects) yet
are widely considered to be measurable. Things like time, budget, patent
ownership, and so on are good examples of things that you cannot touch
but yet are measured. In fact, there is a well-established industry around
measuring so-called intangibles such as copyright and trademark valuation.
But the word “intangible” has also come to mean utterly immeasurable in
any way at all, directly or indirectly. It is in this context that I argue that
intangibles do not exist.
You’ve heard of “intangibles” in your own organization—things that
presumably defy measurement of any type. The presumption of immeasurability is, in fact, so strong that no attempt is even made to make any
observations that might tell you something—anything—about the alleged

3


4

Measurement: The Solution Exists

immeasurable that you might be surprised to learn. You may have run into
one or more of these real-life examples of so-called intangibles:
Management effectiveness
The forecasted revenues of a new product
The public health impact of a new government environmental policy
The productivity of research
The “flexibility” to create new products
The value of information
The risk of bankruptcy
The chance of a given political party winning the White House
The risk of failure of an information technology (IT) project
Quality
Public image
Each of these examples can very well be relevant to some major decision
an organization must make. It could even be the single most important
impact of an expensive new initiative in either business or government
policy. Yet in most organizations, because the specific “intangible” was
assumed to be immeasurable, the decision was not nearly as informed as it
could have been.
One place I’ve seen this many times is in the “steering committees”
that review proposed investments and decide which to accept or reject. The
proposed investments may be related to IT, new product research and development, major real estate development, or advertising campaigns. In some
cases, the committees were categorically rejecting any investment where the
benefits were primarily “soft” ones. Important factors with names like “improved word-of-mouth advertising,” “reduced strategic risk,” or “premium
brand positioning” were being ignored in the evaluation process because
they were considered immeasurable. It’s not as if the idea was being rejected
simply because the person proposing it hadn’t measured the benefit (a valid
objection to a proposal); rather it was believed that the benefit couldn’t possibly be measured—ever. Consequently, some of the most important strategic proposals were being overlooked in favor of minor cost-savings ideas
simply because everyone knew how to measure some things and didn’t
know how to measure others. Equally disturbing, many major investments
were approved with no basis for measuring whether they ever worked at all.
The fact of the matter is that some organizations have succeeded in
analyzing and measuring all of the previously listed items, using methods
that are probably less complicated than you would think. The purpose of
this book is to show organizations two things:
1. Intangibles that appear to be completely intractable can be measured.
2. This measurement can be done in a way that is economically justified.


Intangibles and the Challenge

5

To accomplish these goals, this book will address some common misconceptions about intangibles, describe a “universal approach” to show how
to go about measuring an “intangible,” and provide some interesting methods for particular problems. Throughout, I have attempted to include some
examples (some of which I hope the reader finds inspirational) of how
people have tackled some of the most difficult measurements there are.
Without compromising substance, this book also attempts to make some
of the more seemingly esoteric statistics around measurement as simple as
they can be. Whenever possible, math is converted into simpler charts,
tables, and procedures. Some of the methods are so much simpler than
what is taught in the typical introductory statistics courses that we might be
able to overcome many phobias about the use of quantitative measurement
methods. Readers do not need any advanced training in any mathematical
methods at all. They just need some aptitude for clearly defining problems.
Readers are encouraged to use this book’s Web site at www.
howtomeasureanything.com. The site offers a library of downloadable
spreadsheets for many of the more detailed calculations shown in this book.
There also are additional learning aids, examples, and a discussion board
for questions about the book or measurement challenges in general. The
site also provides a way for me to discuss new technologies or techniques
that were not available when this book was printed.

Yes, I Mean Anything
I have one recommendation for a useful exercise to try. When reading
through the chapters, write down those things you believe are immeasurable
or, at least, you are not sure how to measure. After reading this book, my
goal is that you are able to identify methods for measuring each and every
one of them. And don’t hold back. We will be talking about measuring such
seemingly immeasurable things as the number of fish in the ocean, the value
of a happy marriage, and even the value of a human life. Whether you want
to measure phenomena related to business, government, education, art, or
anything else, the methods herein apply.
With a title like How to Measure Anything, anything less than a multivolume text would be sure to leave out something. My objective does not
include every area of physical science or economics, especially where measurements are well developed. Those disciplines have measurement methods for a variety of interesting problems, and the professionals in those disciplines are already much less inclined even to apply the label “intangible” to
something they are curious about. The focus here is on measurements that
are relevant—even critical—to major organizational decisions and yet don’t
seem to lend themselves to an obvious and practical measurement solution.


6

Measurement: The Solution Exists

If I do not mention your specific measurement problem by name, don’t
conclude that methods relevant to that issue aren’t being covered. The approach I will talk about applies to any uncertainty that has some relevance
to your firm, your community, even your personal life. This extrapolation
should not be difficult. When you studied arithmetic in elementary school,
you may not have covered the solution to 347 times 79 in particular but you
knew that the same procedures applied to any combination of numbers and
operations. So, if your problem happens to be something that isn’t specifically analyzed in this book—such as measuring the value of better product
labeling laws, the quality of a movie script, or effectiveness of motivational
seminars—don’t be dismayed. Just read the entire book and apply the steps
described. Your immeasurable will turn out to be entirely measurable.

The Proposal
Let me begin by stating the three propositions as a way to define and
approach the problem of measurement in business:
1. Management cares about measurements because measurements inform
uncertain decisions.
2. For any decision or set of decisions, there are a large combination of
things to measure and ways to measure them—but perfect certainty is
rarely a realistic option.
3. Therefore, management needs a method to analyze options for reducing
uncertainty about decisions.
Perhaps you think the first two points are too obvious to make. But
while it may seem obvious, few management consultants, performance
metrics experts, or even statisticians approach the problem with the explicit
purpose of supporting defined decisions. Even if they had that squarely in
mind, the last point, at a minimum, is where a lot of business measurement
methods fall short.
It is very useful to see measurement as a type of optimization problem
for reducing uncertainty. Upon reading the first edition of this book, a business school professor remarked that he thought I had written a book about
the somewhat esoteric field called “decision analysis” and disguised it under
a title about measurement so that people from business and government
would read it. That wasn’t my intention when I set out, but I think he hit
the nail on the head. Measurement is about supporting decisions, and there
are even several decisions to make within measurements themselves.
If the decision in question is highly uncertain and has significant consequences if it turns out wrong, then measurements that reduce uncertainty


Intangibles and the Challenge

7

about it have a high value. Nobody should care about measuring something
if it doesn’t inform a significant bet of some kind. Likewise, if measurements
were free, obvious, and instantaneous, we would have no dilemma about
what, how, or even whether to measure.
Granted, a measurement might also be taken because it has its own
market value (e.g., results of a consumer survey) or because it is simply
satisfying a curiosity or will be entertaining (e.g., academic research about
the evolution of clay pottery). But the methods we discuss in the decisionfocused approach to measurement should be useful on those occasions, too.
If a measurement is not informing your decisions, it could still be informing
the decisions of others who are willing to pay for the information. And
if you are an academic curious about what really happened to the wooly
mammoth, then, again, I believe this book will have some bearing on how
you set up the problem.
From here on out, this book addresses three broad issues: why nothing
is really immeasurable, how to set up and define any measurement problem,
and how to use powerful and practical measurement methods to resolve the
problem. The next two chapters of this book build the argument for the first
point: that you can really measure anything. Chapters 4 through 7 set up
the measurement problem by answering questions from the point of view
of supporting specific decisions. We have to answer the question “What is
the real problem/decision/dilemma?” underlying the desired measurement.
We also have to answer the question “What about that problem really needs
to be measured and by how much (to what degree of accuracy/precision)?”
These questions frame the problem in terms of the primary decision the
measurement is meant to resolve and the “microdecisions” that need to be
made within the measurement process itself.
The remainder of the book combines this approach with powerful and
practical empirical methods to reduce uncertainty—some basic, some more
advanced. The final chapter pulls it all together into a solution and describes
how that solution has been applied to real-world problems. Since this approach can apply to anything, the details might sometimes get complicated.
But it is much less complicated than many other initiatives organizations
routinely commit to doing. I know, because I’ve helped many organizations
apply these methods to the really complicated problems: venture capital, IT
portfolios, measuring training, improving homeland security, and more.
In fact, measurements that are useful are often much simpler than people first suspect. I make this point in Chapter 2 by showing how three clever
individuals measured things that were previously thought to be difficult or
impossible to measure.


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